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MM Week 44: Scottish Index of Multiple Deprivation

Barcode charts like this can be useful for seeing small patterns in Data but the visualisation has some issues.

What works well

It shows all the data in a single view with no clicking / interaction

Density of lines shows where most areas lie e.g. Glasgow and North Lanarkshire can quickly be seen as having lots of areas among the most deprived

It is simple and eye catching

What does work as well

No indication of population in each area

Areas tend to blur together

It may be overly simple for the audience

In my first attempt to solve these problems I addressed the second problem above using a jitter (using the random() function)

However it still didn’t address the population issue and given the vast majority of points had similar population with a few outliers (see below) I wondered whether to even address the issue.

Then I realised I could perhaps go back to the original and simply expand on it with a box plot (adding a sort for clarity):

Voila, a simple makeover that improves the original and adds meaning and understanding while staying true to the aims of the original. Time for dinner.

Done and dusted…wasn’t I? If I had any sense I would be but I wanted to find out more about the population of each area. Were the more populated areas also the more deprived?

There have been multiple discussions this week on Twitter about people stepping beyond what Makeover Monday is was intended to be about. However there was story to tell here and I dwelled on it over dinner and, with the recent debates about the aims of Makeover Monday (and data visualisation generally), swirling in my head I wondered what I should do.

I wondered about the rights and wrongs of continuing with a more complex visualisation, should finish here and show how simple Makeover Monday can be? Or should I satisfy my natural curiosity and investigate a chart that, while perhaps more complex, might show ways of presenting data that others hadn’t considered….

I had the data bug and I wanted to tell a story even if it meant diving a bit deeper and perhaps breaking the “rules” of Makeover Monday and spending longer on the visualisation. I caved in and went beyond a simple makeover….sorry Andy K.

Perhaps a scatter plot might work best focusing at the median deprivation of a given area (most deprived at the top by reversing the Rank axis):

Meh, it’s simple but hides a lot of the detail. I added each Data Area and it got too messy as a scatter – but how about a Pareto type chart…

So we can see from the running sum of population (ordered by the most deprived areas first) that lots of people live in deprived areas in Glasgow, but we also see the shape of the other lines is lost given so many people live in Glasgow.

So I added a secondary percent of total, not too complex….this is still within the Desktop II course for Tableau.

Now we were getting somewhere. I can see from the shape of the line whether areas have high proportions of more or less deprived people. Time to add some annotation and explanation….as well as focus on the original 15% most deprived as in the original.

Click on the image below to go to the interactive version. This took me around 3 hours to build following some experimenting with commenting and drop lines that took me down blind (but fun) alleys before I wound back to this.

Conclusion

Makeover Monday is good fun, I happened to have a bit more time tonight and I got the data bug. I could have produced the slightly improved visualisation and stuck with it, but that’s not how storytelling goes. We see different angles and viewpoints, constraining myself to too narrow a viewpoint felt like I was ignoring an itch that just needed scratching.

I’m glad I scratched it. I’m happy with my visualisation but I offer the following critique:

What works well:

it’s more engaging than the original, while it is more complex I hope the annotations offer enough detail to help draw the viewer in and get them exploring.

the purple labels show the user the legend at the same time as describing the data.

there is a story for the user to explore as they click, pop-up text adds extra details.

it adds context about population within areas.

What doesn’t work well:

the user is required to explore with clicks rather than simply scanning the image – a small concession given the improvement in engagement I hope I have made.

the visualisation take some understanding, percent of total cumulative population is a hard concept that many of the public simply won’t understand. The audience for this visualisation is therefore slightly more academic than the original. Would I say this is suitable for publishing on the original site? On balance I probably would say it was. The original website is text / table heavy and clearly intended for researchers not the public and therefore the audience can be expected to be willing to take longer to understand the detail.

2 thoughts on “MM Week 44: Scottish Index of Multiple Deprivation”

– Is this OK for “Makeover Monday” or is it going above and beyond the requirements?
– is this an improvement on the original?

The answer to the first question is a moot point. I’m in the “Yes” camp – of course it’s OK. Yes, it goes above and beyond the suggested hour (and something which took several hours of your time might take several days of a less experienced person’s time). But it’s a true makeover of the original, using no additional data, which improves it. And I don’t feel that anyone who wants to spend extra time on a visualisation (particularly if it’s in a more complex chart showing additional missing insight, rather than taking time to add unnecessary chart “junk”) should be discouraged.

As for whether it’s an improvement – a definite yes. The individual areas/data points might be generally the same population, as designed, and as your first “jitter” shows, but in ironing out the total population number effect from each local authority then we’re not reduced to estimating roughly how “crowded” an area of bars. It gives definite answers on the average spread of deprivation within each local authority where in the original we relied on judgement.

Nothing should discourage anyone with the data bug from going the extra mile in visualisation – it sorts the good from the great!